The Handbook of brain theory and neural networks

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

Complex networks: structure and dynamics

BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010

Sparse coding with an overcomplete basis set: A strategy employed by V1?

Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642

Popular Ensemble Methods: An Empirical Study

Opitz, D., & Maclin, R. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.614 · Cited by 2,953

Interneurons of the neocortical inhibitory system

Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954

Convolutional Sequence to Sequence Learning

Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850

Neurophysiological and Computational Principles of Cortical Rhythms in Cognition

Wang, X.-J. (2010) · Physiological Reviews · doi:10.1152/physrev.00035.2008 · Cited by 1,939

Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology

Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698

Untitled work

(2003) · Cited by 1,680

Dendritic spine geometry is critical for AMPA receptor expression in hippocampal CA1 pyramidal neurons

Matsuzaki, M., Ellis-Davies, G. C. R., Nemoto, T., Miyashita, Y., Iino, M., & Kasai, H. (2001) · Nature Neuroscience · doi:10.1038/nn736 · Cited by 1,563

Sparse coding of sensory inputs

Olshausen, B. A., & Field, D. J. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.07.007 · Cited by 1,404

Event perception: a mind-brain perspective

Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S., & Reynolds, J. R. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.2.273 · Cited by 1,148

Deep supervised, but not unsupervised, models may explain IT cortical representation

Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204

The basal ganglia: a vertebrate solution to the selection problem?

Redgrave, P., Prescott, T. J., & Gurney, K. (1999) · Neuroscience · doi:10.1016/s0306-4522(98)00319-4 · Cited by 1,199

Deep neural networks: a new framework for modeling biological vision and brain information processing

Kriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093

From monkey-like action recognition to human language: An evolutionary framework for neurolinguistics.

Arbib, M. A. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05000038 · Cited by 1,164

Goal-directed and habitual control in the basal ganglia: implications for Parkinson's disease

Redgrave, P., Rodriguez, M., Smith, Y., Rodriguez-Oroz, M. C., Lehericy, S., Bergman, H., Agid, Y., DeLong, M. R., & Obeso, J. A. (2010) · Nature reviews. Neuroscience · doi:10.1038/nrn2915 · Cited by 1,134

Intrinsic Motivation Systems for Autonomous Mental Development

Oudeyer, P.-Y., Kaplan, F., & Hafner, V. V. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2006.890271 · Cited by 1,126

Automated breast ultrasound lesions detection using convolutional neural networks

Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K., & Martí, R. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2731873 · Cited by 1,086

Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory

Zhang, K. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-06-02112.1996 · Cited by 1,001

Data mining for credit card fraud: a comparative study

Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.008 · Cited by 1,108

The BCI competition III: validating alternative approaches to actual BCI problems

Blankertz, B., Muller, K.-R., Krusienski, D. J., Schalk, G., Wolpaw, J. R., Schlogl, A., Pfurtscheller, G., Millan, J. R., Schroder, M., & Birbaumer, N. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875642 · Cited by 927

A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems

Navarro, X., Krueger, T. B., Lago, N., Micera, S., Stieglitz, T., & Dario, P. (2005) · Journal of the Peripheral Nervous System · doi:10.1111/j.1085-9489.2005.10303.x · Cited by 845

Saliency, attention, and visual search: An information theoretic approach

Bruce, N. D. B., & Tsotsos, J. K. (2009) · Journal of Vision · doi:10.1167/9.3.5 · Cited by 855

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

Liu, R., Lehman, J., Molino, P., Such, F. P., Frank, E., Sergeev, A., & Yosinski, J. (2018) · arXiv · doi:10.48550/arxiv.1807.03247 · Cited by 784

From reinforcement learning models to psychiatric and neurological disorders

Maia, T. V., & Frank, M. J. (2011) · Nature Neuroscience · doi:10.1038/nn.2723 · Cited by 722

Metalearning and neuromodulation

Doya, K. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00044-8 · Cited by 704

Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors

Deco, G., & Jirsa, V. K. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.2523-11.2012 · Cited by 699

Neural Network for Graphs: A Contextual Constructive Approach

Micheli, A. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2010350 · Cited by 639

A Neural Circuit Mechanism Integrating Motivational State with Memory Expression in Drosophila

Krashes, M. J., DasGupta, S., Vreede, A., White, B., Armstrong, J. D., & Waddell, S. (2009) · Cell · doi:10.1016/j.cell.2009.08.035 · Cited by 598

The application of artificial neural networks to the analysis of remotely sensed data

Mas, J. F., & Flores, J. J. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701352154 · Cited by 616

Behavioral functions of the mesolimbic dopaminergic system: An affective neuroethological perspective

Alcaro, A., Huber, R., & Panksepp, J. (2007) · Brain Research Reviews · doi:10.1016/j.brainresrev.2007.07.014 · Cited by 616

Assessing PM 2.5 Exposures with High Spatiotemporal Resolution across the Continental United States

Di, Q., Kloog, I., Koutrakis, P., Lyapustin, A., Wang, Y., & Schwartz, J. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b06121 · Cited by 510

A unified framework for addiction: Vulnerabilities in the decision process

Redish, A. D., Jensen, S., & Johnson, A. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0800472x · Cited by 501

Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data

Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J., & Leach, M. O. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.277 · Cited by 488

Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning

Kasabov, N. (2001) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/3477.969494 · Cited by 482

Multi-input CNN-GRU based human activity recognition using wearable sensors

Dua, N., Singh, S. N., & Semwal, V. B. (2021) · Computing · doi:10.1007/s00607-021-00928-8 · Cited by 432

Relevance of Error: What Drives Motor Adaptation?

Wei, K., & Körding, K. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90545.2008 · Cited by 422

The neurobehavioral nature of fishes and the question of awareness and pain.

Rose, J. D. (2002) · Reviews in Fisheries Science · doi:10.1080/20026491051668 · Cited by 413

Neural representations and mechanisms for the performance of simple speech sequences

Bohland, J. W., Bullock, D., & Guenther, F. H. (2010) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21306 · Cited by 393

Age of acquisition effects in adult lexical processing reflect loss of plasticity in maturing systems: Insights from connectionist networks.

Ellis, A. W., & Lambon Ralph, M. A. (2000) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037//0278-7393.26.5.1103 · Cited by 406

Statistical and machine learning models in credit scoring: A systematic literature survey

Dastile, X., Celik, T., & Potsane, M. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106263 · Cited by 404

The illiterate brain. Learning to read and write during childhood influences the functional organization of the adult brain

Castro-Caldas, A. (1998) · Brain · doi:10.1093/brain/121.6.1053 · Cited by 395

Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling

Redish, A. D., Jensen, S., Johnson, A., & Kurth-Nelson, Z. (2007) · Psychological Review · doi:10.1037/0033-295x.114.3.784 · Cited by 346

A deep learning approach for the analysis of masses in mammograms with minimal user intervention

Dhungel, N., Carneiro, G., & Bradley, A. P. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.01.009 · Cited by 336

Ensemble methods for spoken emotion recognition in call-centres

Morrison, D., Wang, R., & De Silva, L. C. (2007) · Speech Communication · doi:10.1016/j.specom.2006.11.004 · Cited by 320

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

Böhle, M., Eitel, F., Weygandt, M., & Ritter, K. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00194 · Cited by 291

On rhythmic and discrete movements: reflections, definitions and implications for motor control

Hogan, N., & Sternad, D. (2007) · Experimental Brain Research · doi:10.1007/s00221-007-0899-y · Cited by 285

Exploiting multi-channels deep convolutional neural networks for multivariate time series classification

Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-015-4478-2 · Cited by 273

Convolutional neural network-based hidden Markov models for rolling element bearing fault identification

Wang, S., Xiang, J., Zhong, Y., & Zhou, Y. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.12.027 · Cited by 269

Bayesian Computation in Recurrent Neural Circuits

Rao, R. P. N. (2004) · Neural Computation · doi:10.1162/08997660460733976 · Cited by 252

Sensory acquisition in active sensing systems

Nelson, M. E., & MacIver, M. A. (2006) · Journal of Comparative Physiology A · doi:10.1007/s00359-006-0099-4 · Cited by 247

The time course of visual processing: Backward masking and natural scene categorisation

Bacon-Macé, N., Macé, M. J.-M., Fabre-Thorpe, M., & Thorpe, S. J. (2005) · Vision Research · doi:10.1016/j.visres.2005.01.004 · Cited by 236

Sensory and decision-related activity propagate in a cortical feedback loop during touch perception

Kwon, S. E., Yang, H., Minamisawa, G., & O'Connor, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4356 · Cited by 210

Phase-response curves and synchronized neural networks

Smeal, R. M., Ermentrout, G. B., & White, J. A. (2010) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2009.0292 · Cited by 198

Moving from cognition to behavior: What the research says.

Johnson, R. E., Chang, C.-H., & Lord, R. G. (2006) · Psychological Bulletin · doi:10.1037/0033-2909.132.3.381 · Cited by 193

Current trends in the development of intelligent unmanned autonomous systems

Zhang, T., Li, Q., Zhang, C.-S., Liang, H.-W., Li, P., Wang, T.-M., Li, S., Zhu, Y.-L., & Wu, C. (2017) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1601650 · Cited by 184

Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

Liu, Y., Tan, X., Liang, J., Han, H., Xiang, P., & Yan, W. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202214271 · Cited by 174

Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images

Wu, G., Kim, M., Wang, Q., Gao, Y., Liao, S., & Shen, D. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_80 · Cited by 149

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Geiger, M., Spigler, S., d'Ascoli, S., Sagun, L., Baity-Jesi, M., Biroli, G., & Wyart, M. (2019) · Physical Review E · doi:10.1103/physreve.100.012115 · Cited by 143

Modeling signal and background components of electrosensory scenes

Chen, L., House, J. L., Krahe, R., & Nelson, M. E. (2005) · Journal of Comparative Physiology A · doi:10.1007/s00359-004-0587-3 · Cited by 142

Untitled work

Peng (2018) · doi:10.1145/3214277 · Cited by 134

Human-Factors-in-Driving-Loop: Driver Identification and Verification via a Deep Learning Approach Using Psychological Behavioral Data

Xu, J., Pan, S., Sun, P. Z. H., Hyeong Park, S., & Guo, K. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3225782 · Cited by 120

Detecting adaptive introgression in human evolution using convolutional neural networks

Gower, G., Picazo, P. I., Fumagalli, M., & Racimo, F. (2021) · eLife · doi:10.7554/elife.64669 · Cited by 95

Intelligence Through Interaction: Towards a Unified Theory for Learning

Tan, A.-H., Carpenter, G. A., & Grossberg, S. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72383-7_128 · Cited by 101

Unsupervised learning of invariant representations

Anselmi, F., Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., & Poggio, T. (2016) · Theoretical Computer Science · doi:10.1016/j.tcs.2015.06.048 · Cited by 88

Dynamics of Memory Representations in Networks with Novelty-Facilitated Synaptic Plasticity

Blumenfeld, B., Preminger, S., Sagi, D., & Tsodyks, M. (2006) · Neuron · doi:10.1016/j.neuron.2006.08.016 · Cited by 79

Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble

Santos-Garcı́a, G., Varela, G., Novoa, N., & Jiménez, M. F. (2004) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(03)00059-9 · Cited by 70

Learning hidden patterns from patient multivariate time series data using convolutional neural networks: A case study of healthcare cost prediction

Morid, M. A., Sheng, O. R. L., Kawamoto, K., & Abdelrahman, S. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103565 · Cited by 49

Generative models for active vision

Parr, T., Sajid, N., Da Costa, L., Mirza, M. B., & Friston, K. J. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.651432 · Cited by 46

Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering

Minot, M., & Reddy, S. T. (2024) · Cell Systems · doi:10.1016/j.cels.2023.12.003 · Cited by 35

Parallel and distributed neural models of the ideomotor principle: An investigation of imitative cortical pathways

Sauser, E. L., & Billard, A. G. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.02.003 · Cited by 33

Free range startups? Market scope, academic founders, and the role of general knowledge in AI

Chattopadhyay, S., Honoré, F., & Won, S. (2025) · Strategic Management Journal · doi:10.1002/smj.3685 · Cited by 30

Link Prediction for Completing Graphical Software Models Using Neural Networks

Leblebici, O., Tuglular, T., & Belli, F. (2023) · IEEE Access · doi:10.1109/access.2023.3323591 · Cited by 1